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논문 기본 정보

자료유형
학술저널
저자정보
June-Tae Han (Korea Student Aid Foundation) Il-Su Park (Dong-eui University)
저널정보
한국데이터정보과학회 한국데이터정보과학회지 한국데이터정보과학회지 제36권 제1호
발행연도
2025.1
수록면
163 - 177 (15page)
DOI
10.7465/jkdi.2025.36.1.163

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초록· 키워드

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This study aims to develop a prediction model for assessing depression risk. Depression-related risk factors were identified from prior studies, and the most influential factors were selected using a feature selection method based on four machine learning techniques: random forest, XGBoost, AdABoost, and gradient, boosting. The random forest algorithm achieved the highest, receiver operation characteristic (ROC) curve (0.8407) in classifying depression among the four machine learning algorithms. The variables were derived from the Korea community health survey (KCHS) 2022 by the Korea disease control and prevention agency (KDCA) and used as input variables, with depression status as the target, variable. A weighted logistic regression model was employed for prediction. Based on feature importance rankings from four machine learning techniques, the combined key risk factors included economic activity, monthly household income, marital status, walking habits, subjective stress perception, subjective health status, number of cultural infrastructures, unemployment, rate, suicide rate, and number of doctors. In the prediction model, subjective stress perception (OR: 9.65) was the most significant risk factor, followed by subjective health status (OR: 3.38). The use of machine learning techniques for variable selection effectively addresses the challenge of interpretability in prediction models. This approach demonstrates great potential for future healthcare-related disease risk prediction models.

목차

Abstract
1. Introduction
2. Materials and methods
3. Results
4. Conclusion and discussion
References

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